Clustering is a central problem in unsupervised machine learning and
data mining. Given a dataset and information regarding the similarity
of pairs of elements, a “good” clustering is a partition of the
elements into groups such that similar elements belong to the same
group, while dissimilar elements belong to different groups. Since
its introduction by Bansal, Blum, and Chawla [1]}, Correlation Clustering has
been one of the most widely studied formulations for clustering. Given
a graph \(G = (V, E)\)  where each edge is either labeled \(+\)  or \(-\) , the
goal is to find a clustering (partition) \((V_1, \dots , V_k)\)  of \(V\) 
that minimizes the number of unsatisfied edges, namely the \(+\) edges
between different clusters and the \(-\) edges within the same cluster.
Thanks to the simplicity and modularity of the formulation, Correlation Clustering has
found a spectacular number of applications, e.g., finding clustering
ensembles [2]}, duplicate detection
[3]}, community mining [4]},
disambiguation tasks [5]}, automated labelling
[6]}, [7]} and many more.
